Drug related deaths in the United States of America

Drug related deaths in the US - A case study

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Overview and Motivation

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Summary of study

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Let´s jump right in an take a look at the total number of deaths by drugs scaled for 10 000 inhabitants per state.

We scale the data so the relationship between the number of deaths become equal for the number of inhabitants in every state. Then we divide all of the states into four regions:

Northeast

Midwest

South

West

Deaths by drugs for all states compared to one another in 2016:

How did they rank in 2017?

State Deaths per 10 000 inhabitant Rank
District of Columbia 67.584604 1
West Virginia 63.899986 2
Ohio 52.540842 3
Pennsylvania 50.511744 4
Maryland 45.563919 5
Kentucky 42.629676 6
Delaware 41.814790 7
New Hampshire 41.138074 8
Massachusetts 37.697788 9
Rhode Island 37.616585 10
Connecticut 35.098534 11
Maine 33.640066 12
Florida 33.470295 13
Tennessee 31.959776 14
Indiana 31.790156 15
New Jersey 31.742316 16
Michigan 31.503831 17
Nevada 29.179073 18
New Mexico 28.907077 19
Missouri 27.873136 20
North Carolina 26.844881 21
Louisiana 26.687018 22
Vermont 26.184265 23
Oklahoma 25.624791 24
Arizona 25.016141 25
South Carolina 24.457369 26
Utah 24.263073 27
Illinois 24.085293 28
Wisconsin 23.718754 29
Colorado 21.483269 30
Alaska 21.235613 31
Virginia 20.664846 32
Alabama 19.759101 33
Washington 17.855062 34
Georgia 17.736893 35
Idaho 16.348825 36
Hawaii 16.195716 37
Arkansas 16.135175 38
Minnesota 15.290441 39
Wyoming 15.034998 40
Oregon 14.963396 41
California 14.773633 42
New York 14.671981 43
Montana 13.945833 44
North Dakota 13.132557 45
Mississippi 13.078333 46
Kansas 13.030601 47
Texas 12.954382 48
Iowa 12.888233 49
South Dakota 10.545914 50
Nebraska 8.091983 51

What is happening in district of columbia???

This is some serious numbers. The total average drug deaths per state for 2015 is 11 585 and worse, it increased to 15 856 in 2017. This is an increase of 36.87% from 2015 to 2017, implefying that it is a serious problem in the US. The total number of deaths by drugs was 590 825 in 2015, 682 084 in 2016 and 808 661 in 2017. This equals a total of 2 081 570 people just for the three years this case study is studying. That´s the same as the total population of Slovenia to put things in perspective. Wiped out over three years.

But how many drug deaths do we find compared to all deaths?

A small percentage: 1.9 % of all deaths in average in the US was drug related in 2015 2.2 % of all deaths in average in the US was drug related in 2016 2.45 % of all deaths in average in the US was drug related in 2017

Low income and high unemployment will result in high overdose rate?

2016 numbers

Our assupmption is that low income and high unemployment equals high od-rate. So we take a look at the top 10 states of high od-rate, lowest income and highest unemployment.

State Deaths pr 10000 inhabitants Rank
West Virginia 53 1
New Hampshire 39 2
Ohio 39 3
District of Columbia 38 4
Rhode Island 38 5
Kentucky 36 6
Pennsylvania 36 7
Massachusetts 35 8
Maryland 34 9
Connecticut 30 10
State Median household income 2016 Rank
Mississippi 40528 51
Arkansas 42336 50
West Virginia 42644 49
Alabama 44758 48
Kentucky 44811 47
Louisiana 45652 46
New Mexico 45674 45
Tennessee 46574 44
South Carolina 46898 43
Oklahoma 48038 42
State The unemployment rate in percent of states labor force 2016 Rank
Alaska 6.9 51
New Mexico 6.7 50
District of Columbia 6.1 48
West Virginia 6.1 48
Louisiana 6.0 47
Alabama 5.9 46
Illinois 5.8 44
Mississippi 5.8 44
Nevada 5.7 43
California 5.5 42

As we can se this is not allways the case. West Wirginia stands out and is represented badly in all three categories. The people working in DC make good money, but both unemployment and od-rate are high. Kentucky is represented in the table for low income. Maryland is the states with highest income the last couple of years by a solid margin, and is the 9th worst place for od in the country. Othervise the similarities was not as strong as expected.

But high income and low unemployment would equal low od-rate right? Not for Maryland thats for sure.

State Deaths pr 10000 inhabitants Rank
Nebraska 7.5 1
South Dakota 9.3 2
North Dakota 10.9 3
Texas 11.7 4
Iowa 11.9 5
New York 12.6 6
Kansas 13.0 7
Mississippi 13.2 8
Minnesota 13.9 9
Montana 14.2 10
State Median household income 2016 Rank
Maryland 76067 1
Alaska 74444 2
New Jersey 73702 3
District of Columbia 72935 4
Hawaii 71977 5
Connecticut 71755 6
Massachusetts 70954 7
New Hampshire 68485 8
Virginia 66149 9
California 63783 10
State The unemployment rate in percent of states labor force 2016 Rank
New Hampshire 2.9 1
Hawaii 2.9 1
South Dakota 3.0 3
North Dakota 3.1 4
Nebraska 3.1 4
Vermont 3.2 6
Colorado 3.3 7
Utah 3.4 8
Iowa 3.6 9
Maine 3.8 10

What about the states with High(good) and low(bad) temperatures?

State TempC Rank
Florida 22 1
Hawaii 21 2
Louisiana 19 3
Texas 18 4
Georgia 18 5
Mississippi 17 6
Alabama 17 7
South Carolina 17 8
Arkansas 16 9
Arizona 16 10
State TempC Rank
Alaska -3.0 1
North Dakota 4.7 2
Maine 5.0 3
Minnesota 5.1 4
Wyoming 5.6 5
Montana 5.9 6
Vermont 6.1 7
Wisconsin 6.2 8
New Hampshire 6.6 9
Michigan 6.9 10

blablabla

## [1] 0.87
## [1] 0.25
## [1] 0.012
## [1] 0.32

Remember that number of incidents do not equal number of death, since people could be affected by several drugs and all of them would be registerd.

What will a linear regression model say about OD dependent on income, weather and unemployment?

## 
## Call:
## lm(formula = OD16_relation ~ Median_income16 + PrecipitationMM + 
##     Clear_days + Rate2016, data = comparedata)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.001323 -0.000648 -0.000169  0.000505  0.002511 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      3.35e-04   1.45e-03    0.23    0.818  
## Median_income16  1.02e-08   1.48e-08    0.69    0.496  
## PrecipitationMM  4.60e-07   3.84e-07    1.20    0.238  
## Clear_days      -3.18e-06   5.20e-06   -0.61    0.544  
## Rate2016         2.68e-04   1.34e-04    2.01    0.051 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9e-04 on 45 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.145,  Adjusted R-squared:  0.0687 
## F-statistic:  1.9 on 4 and 45 DF,  p-value: 0.126

Interactive map with summary of every state

Heatmap of drug ratio US

The BIG DATA

Since this is a case study, it would be unreasenable of us not to show you the data we have been working on as a part of this task. The data is a result of scraping, gathering, massaging and arranging multiple data sets with tens of thousand observations. This is the end result the task has been created with. See for yourself, and feel free to do your own calculations. The code for everything is on our github. Thanks for reading. - Ørjan, Preben and Daniel.